Diverse Non-Genetic Allele Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain [Primate Daughters]
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ABSTRACT:
ORGANISM(S): Macaca fascicularis
PROVIDER: GSE93786 | GEO | 2017/02/28
SECONDARY ACCESSION(S): PRJNA362511
REPOSITORIES: GEO
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Project description:Genotype parent macaques. This allows us to distinguish maternal and paternal expression in the transicriptomes from the offspring. This also allows us to create a transcriptome index for each daughter that reflects the possible heterozygous SNPs wiith IUPAC codes. This then allows for an alignment that is not biased towords the maternally or paternally expressed reads.
2017-02-28 | GSE93785 | GEO
Project description:Interactions between genetic and epigenetic effects shape brain function, behavior, and the risk for mental illness. Random X inactivation and genomic imprinting are epigenetic allelic effects that are well known to influence genetic architecture and disease risk. Less is known about the nature, prevalence, and conservation of other potential epigenetic allelic effects in vivo in the mouse and primate brain. Here we devise genomics, in situ hybridization, and mouse genetics strategies to uncover diverse allelic effects in the brain that are not caused by imprinting or genetic variation. We found allelic effects that are developmental stage and cell type specific, that are prevalent in the neonatal brain, and that cause mosaics of monoallelic brain cells that differentially express wild-type and mutant alleles for heterozygous mutations. Finally, we show that diverse non-genetic allelic effects that impact mental illness risk genes exist in the macaque and human brain. Our findings have potential implications for mammalian brain genetics. VIDEO ABSTRACT.
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Project description:Diverse Non-Genetic Allele Specific Expression Effects Shape Genetic Architecture at the Cellular Level in the Mammalian Brain
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Project description:For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop.
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Project description:Genome-wide transcriptional profiling was used to characterize the molecular underpinnings of neocortical organization in rhesus macaque, including cortical areal specialization and laminar cell-type diversity. Microarray analysis of individual cortical layers across sensorimotor and association cortices identified robust and specific molecular signatures for individual cortical layers and areas, prominently involving genes associated with specialized neuronal function. Overall, transcriptome-based relationships were related to spatial proximity, being strongest between neighboring cortical areas and between proximal layers. Primary visual cortex (V1) displayed the most distinctive gene expression compared to other cortical regions in rhesus and human, both in the specialized layer 4 as well as other layers. Laminar patterns were more similar between macaque and human compared to mouse, as was the unique V1 profile that was not observed in mouse. These data provide a unique resource detailing neocortical transcription patterns in a nonhuman primate with great similarity in gene expression to human.
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Project description:BackgroundBackfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds.MethodsData comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10-6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model.ResultsWe found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN.ConclusionsOur results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.
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